Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models
Authors: Puqian Wang, Nikos Zarifis, Ilias Diakonikolas, Jelena Diakonikolas
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper is theoretical in nature and does not include experiments. |
| Researcher Affiliation | Academia | Puqian Wang Department of Computer Science University of Wisconsin, Madison pwang333@wisc.edu Nikos Zarifis Department of Computer Science University of Wisconsin, Madison zarifis@wisc.edu Ilias Diakonikolas Department of Computer Science University of Wisconsin, Madison ilias@cs.wisc.edu Jelena Diakonikolas Department of Computer Science University of Wisconsin, Madison jelena@cs.wisc.edu |
| Pseudocode | Yes | Algorithm 1 k-Chow Tensor PCA (page 4) and Algorithm 2 Riemannian GD with Warm-start (page 6). |
| Open Source Code | No | The paper is theoretical in nature and does not conduct experiments, nor does it provide any statement or link for open-source code release. |
| Open Datasets | No | The paper is theoretical and does not mention the use of any specific publicly available datasets for empirical training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe any training, validation, or test dataset splits, as it does not conduct experiments. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings for empirical runs. |